SoroushVahidi/scidocs huggingface dataset:
Neither more nor less: Optimizing thread-level parallelism for GPGPUs
(2013)

General-purpose graphics processing units (GPGPUs) are at their best in accelerating computation by exploiting abundant thread-level parallelism (TLP) offered by many classes of HPC applications. To facilitate such high TLP, emerging programming models like CUDA and OpenCL allow programmers to create work abstractions in terms of smaller work units, called cooperative thread arrays (CTAs). CTAs are groups of threads and can be executed in any order, thereby providing ample opportunities for TLP. The state-of-the-art GPGPU schedulers allocate maximum possible CTAs per-core (limited by available on-chip resources) to enhance performance by exploiting TLP. However, we demonstrate in this paper that executing the maximum possible number of CTAs on a core is not always the optimal choice from the performance perspective. High number of concurrently executing threads might cause more memory requests to be issued, and create contention in the caches, network and memory, leading to long stalls at the cores. To reduce resource contention, we propose a dynamic CTA scheduling mechanism, called DYNCTA, which modulates the TLP by allocating optimal number of CTAs, based on application characteristics. To minimize resource contention, DYNCTA allocates fewer CTAs for applications suffering from high contention in the memory sub-system, compared to applications demonstrating high throughput. Simulation results on a 30-core GPGPU platform with 31 applications show that the proposed CTA scheduler provides 28% average improvement in performance compared to the existing CTA scheduler.
Mechatronic model of a novel slotless permanent magnet DC-motor with air gap winding design
(2013)

This paper presents a mechatronic model of a novel slotless permanent magnet DC-motor with air gap winding. Besides technical advantages of this type of motor like high power density, high torque, very low weight and high efficiency, the motor design allows a very precise and efficient modelling with limited effort. A nonlinear model of magnetic field density can be extracted from a detailed nonlinear FE-model build in ANSYS/Maxwell, approximated by Fourier series and then used to model driving torque and back EMF, representing the coupling between electrical and mechanical subsystems. Analytically founded numerical models for driving torque and back EMF will be given. Real geometry of the phase winding is taken into account to improve model accuracy. The electrical subsystem will be described as coupled three phase system, whose parameters can also be extracted from the nonlinear FE-model with high accuracy. Together with a mechanical model of the rotor a MATLAB/Simulink model is build and extended by models of the hall sensors to detect rotor position and commutation logic to control the HEX-Bridge during operation. Finally, results of a complex simulation model, based on the parameters of the prototype of a wheel-hub motor, implementing the new motor design, are getting shown. Simulation results compare very well to measured data. Simulation time is very short due to the efficient approximation of magnetic flux density.
Reverse Engineering of Embedded Software Using Syntactic Pattern Recognition
(2006)

When a secure component executes sensitive operations, the information carried by the power consumption can be used to recover secret information. Many different techniques have been developped to recover this secret, but only few of them focus on the recovering of the executed code itself. Indeed, the code knowledge acquired through this step of Simple Power Analysis (SPA) can help to identify implementation weaknesses and to improve further kinds of attacks. In this paper we present a new approach improving the SPA based on a pattern recognition methodology, that can be used to automatically identify the processed instructions that leak through power consumption. We firstly process a geometrical classification with chosen instructions to enable the automatic identification of any sequence of instructions. Such an analysis is used to reverse general purpose code executions of a recent secure component.
Communicating Algorithmic Process in Online Behavioral Advertising
(2018)

Advertisers develop algorithms to select the most relevant advertisements for users. However, the opacity of these algorithms, along with their potential for violating user privacy, has decreased user trust and preference in behavioral advertising. To mitigate this, advertisers have started to communicate algorithmic processes in behavioral advertising. However, how revealing parts of the algorithmic process affects users' perceptions towards ads and platforms is still an open question. To investigate this, we exposed 32 users to why an ad is shown to them, what advertising algorithms infer about them, and how advertisers use this information. Users preferred interpretable, non-creepy explanations about why an ad is presented, along with a recognizable link to their identity. We further found that exposing users to their algorithmically-derived attributes led to algorithm disillusionment---users found that advertising algorithms they thought were perfect were far from it. We propose design implications to effectively communicate information about advertising algorithms.
Effective Heart Sound Segmentation and Murmur Classification Using Empirical Wavelet Transform and Instantaneous Phase for Electronic Stethoscope
(2017)

Accurate measurement of heart sound and murmur parameters is of great importance in the automated analysis of phonocardiogram (PCG) signals. In this paper, we propose a novel unified PCG signal delineation and murmur classification method without the use of reference signal for automatic detection and classification of heart sounds and murmurs. The major components of the proposed method are the empirical wavelet transform-based PCG signal decomposition for discriminating heart sounds from heart murmurs and suppressing background noises, the Shannon entropy envelope extraction, the instantaneous phase-based boundary determination, heart sound and murmur parameter extraction, the systole/diastole discrimination and the decision rules-based murmur classification. The accuracy and robustness of the proposed method is evaluated using a wide variety of normal and abnormal PCG signals taken from the standard PCG databases, including PASCAL heart sounds challenge database, PhysioNet/CinC challenge heart sound database, and real-time PCG signals. Evaluation results show that the proposed method achieves an average sensitivity (Se) of 94.38%, positive predictivity (Pp) of 97.25%, and overall accuracy (OA) of 91.92% for heart sound segmentation and Se of 97.58%, Pp of 96.46%, and OA of 94.21% in detecting the presence of heart murmurs for SNR of 10 dB. The method yields an average classification accuracy of 95.5% for the PCG signals with SNR of 20 dB. Results show that the proposed method outperforms other existing heart sound segmentation and murmur classification methods.
Color categories: Evidence for the cultural relativity hypothesis
(2005)

The question of whether language affects our categorization of perceptual continua is of particular interest for the domain of color where constraints on categorization have been proposed both within the visual system and in the visual environment. Recent research (Roberson, Davies, & Davidoff, 2000; Roberson et al., in press) found substantial evidence of cognitive color differences between different language communities, but concerns remained as to how representative might be a tiny, extremely remote community. The present study replicates and extends previous findings using additional paradigms among a larger community in a different visual environment. Adult semi-nomadic tribesmen in Southern Africa carried out similarity judgments, short-term memory and long-term learning tasks. They showed different cognitive organization of color to both English and another language with the five color terms. Moreover, Categorical Perception effects were found to differ even between languages with broadly similar color categories. The results provide further evidence of the tight relationship between language and cognition.
The Airport Gate Assignment Problem: Mathematical Model and a Tabu Search Algorithm
(2001)

In this paper, we consider an Airport Gate Assignment Problem that dynamically assigns airport gates to scheduled ights based on passengers' daily origin and destination ow data. The objective of the problem is to minimize the overall connection times that passengers walk to catch their connection ights. We formulate this problem as a mixed 0-1 quadratic integer programming problem and then reformulate it as a mixed 0-1 integer problem with a linear objective function and constraints. We design a simple tabu search meta-heuristic to solve the problem. The algorithm exploits the special properties of di erent types of neighborhood moves, and create highly e ective candidate list strategies. We also address issues of tabu short term memory, dynamic tabu tenure, aspiration rule, and various intensi cation and diversi cation strategies. Preliminary computational experiments are conducted and the results are presented and analyzed.
A Continuous Beam Steering Slotted Waveguide Antenna Using Rotating Dielectric Slabs
(2018)

The design, simulation and measurement of a beam steerable slotted waveguide antenna operating in X band are presented. The proposed beam steerable antenna consists of a standard rectangular waveguide (RWG) section with longitudinal slots in the broad wall. The beam steering in this configuration is achieved by rotating two dielectric slabs inside the waveguide and consequently changing the phase of the slots excitations. In order to confirm the usefulness of this concept, a non-resonant 20-slot waveguide array antenna with an element spacing of d = 0.58λ0 has been designed, built and measured. A 14o beam scanning from near broadside (θ = 4o) toward end-fire (θ = 18o) direction is observed. The gain varies from 18.33 dB to 19.11 dB which corresponds to the radiation efficiencies between 95% and 79%. The side-lobe level is -14 dB at the design frequency of 9.35 GHz. The simulated co-polarized realized gain closely matches the fabricated prototype patterns.
End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient
(2017)

Learning a goal-oriented dialog policy is generally performed offline with supervised learning algorithms or online with reinforcement learning (RL). Additionally, as companies accumulate massive quantities of dialog transcripts between customers and trained human agents, encoder-decoder methods have gained popularity as agent utterances can be directly treated as supervision without the need for utterance-level annotations. However, one potential drawback of such approaches is that they myopically generate the next agent utterance without regard for dialog-level considerations. To resolve this concern, this paper describes an offline RL method for learning from unannotated corpora that can optimize a goal-oriented policy at both the utterance and dialog level. We introduce a novel reward function and use both on-policy and off-policy policy gradient to learn a policy offline without requiring online user interaction or an explicit state space definition.
Post-Stroke Rehabilitation with the Rutgers Ankle System: A Case Study
(2001)

The Rutgers Ankle is a Stewart platform-type haptic interface designed for use in rehabilitation. The system supplies six-degree-of-freedom (DOF) resistive forces on the patient's foot, in response to virtual reality-based exercises. The Rutgers Ankle controller contains an embedded Pentium board, pneumatic solenoid valves, valve controllers, and associated signal conditioning electronics. The rehabilitation exercise used in our case study consists of piloting a virtual airplane through loops. The exercise difficulty can be selected based on the number and placement of loops, the airplane speed in the virtual environment, and the degree of resistance provided by the haptic interface. Exercise data is stored transparently, in real time, in an Oracle database. These data consist of ankle position, forces, and mechanical work during an exercise, and over subsequent rehabilitation sessions. The number of loops completed and the time it took to do that are also stored online. A case study is presented of a patient nine months post-stroke using this system. Results showed that, over six rehabilitation sessions, the patient improved on clinical measures of strength and endurance, which corresponded well with torque and power output increases measured by the Rutgers Ankle. There were also substantial improvements in task accuracy and coordination during the simulation and the patient's walking and stair-climbing ability.
The Development and Validation of a Measure of Student Attitudes Toward Science , Technology , Engineering , and Math ( S-STEM )
(2015)

Using an iterative design along with multiple methodological approaches and a large representative sample, this study presents reliability, validity, and fairness evidence for two surveys measuring student attitudes toward science, technology, engineering, and math (S-STEM) and interest in STEM careers for (a) 4ththrough 5th-grade students (Upper Elementary S-STEM) and (b) 6ththrough 12th-grade students (Middle/High S-STEM). Findings from exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) suggested the use of a four-factor structure to measure student attitudes toward science, math, engineering/technology, and 21st century skills. Subject matter experts and literature reviews provided evidence of content validity. Reliability levels were high for both versions. Furthermore, both the Upper Elementary S-STEM and Middle/High S-STEM Surveys demonstrated evidence of configural, metric, and scalar invariance across grade levels, races/ethnicities, and genders. The findings support the validity of interpretations and inferences made from scores on the instruments’ items and subscales.
VCDB: A Large-Scale Database for Partial Copy Detection in Videos
(2014)

The task of partial copy detection in videos aims at finding if one or more segments of a query video have (transformed) copies in a large dataset. Since collecting and annotating large datasets of real partial copies are extremely time-consuming, previous video copy detection research used either small-scale datasets or large datasets with simulated partial copies by imposing several pre-defined transformations (e.g., photometric or geometric changes). While the simulated datasets were useful for research, it is unknown how well the techniques developed on such data work on real copies, which are often too complex to be simulated. In this paper, we introduce a large-scale video copy database (VCDB) with over 100,000 Web videos, containing more than 9,000 copied segment pairs found through careful manual annotation. We further benchmark a baseline system on VCDB, which has demonstrated state-of-the-art results in recent copy detection research. Our evaluation suggests that existing techniques—which have shown near-perfect results on the simulated benchmarks—are far from satisfactory in detecting complex real copies. We believe that the release of VCDB will largely advance the research around this challenging problem.
Applying Winnow to Context-Sensitive Spelling Correction
(1996)

Multiplicative weight-updating algorithms such as Winnow have been studied extensively in the COLT literature, but only recently have people started to use them in applications. In this paper, we apply a Winnow-based algorithm to a task in natural language: contextsensitive spelling correction. This is the task of xing spelling errors that happen to result in valid words, such as substituting to for too, casual for causal, and so on. Previous approaches to this problem have been statistics-based; we compare Winnow to one of the more successful such approaches, which uses Bayesian classi ers. We nd that: (1) When the standard (heavily-pruned) set of features is used to describe problem instances, Winnow performs comparably to the Bayesian method; (2) When the full (unpruned) set of features is used, Winnow is able to exploit the new features and convincingly outperform Bayes; and (3) When a test set is encountered that is dissimilar to the training set, Winnow is better than Bayes at adapting to the unfamiliar test set, using a strategy we will present for combining learning on the training set with unsupervised learning on the (noisy) test set. In Machine Learning: Proceedings of the 13th International Conference, Lorenza Saitta, ed., Morgan Kaufmann, San Francisco, CA, 1996, pages 182{190. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonpro t educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Information Technology Center America; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Information Technology Center America. All rights reserved. Copyright c Mitsubishi Electric Information Technology Center America, 1996 201 Broadway, Cambridge, Massachusetts 02139 Dept. of Appl. Math. & CS, Weizmann Institute of Science, Rehovot 76100, Israel. 1. First printing, TR96-07, April 1996
Illuminating cell signalling with optogenetic tools
(2014)

The light-based control of ion channels has been transformative for the neurosciences, but the optogenetic toolkit does not stop there. An expanding number of proteins and cellular functions have been shown to be controlled by light, and the practical considerations in deciding between reversible optogenetic systems (such as systems that use light-oxygen-voltage domains, phytochrome proteins, cryptochrome proteins and the fluorescent protein Dronpa) are well defined. The field is moving beyond proof of concept to answering real biological questions, such as how cell signalling is regulated in space and time, that were difficult or impossible to address with previous tools.
Anytime parallel density-based clustering
(2018)

The density-based clustering algorithm DBSCAN is a state-of-the-art data clustering technique with numerous applications in many fields. However, DBSCAN requires neighborhood queries for all objects and propagation of labels from object to object. This scheme is time consuming and thus limits its applicability for large datasets. In this paper, we propose a novel anytime approach to cope with this problem by reducing both the range query and the label propagation time of DBSCAN. Our algorithm, called AnyDBC, compresses the data into smaller density-connected subsets called primitive clusters and labels objects based on connected components of these primitive clusters to reduce the label propagation time. Moreover, instead of passively performing range queries for all objects as in existing techniques, AnyDBC iteratively and actively learns the current cluster structure of the data and selects a few most promising objects for refining clusters at each iteration. Thus, in the end, it performs substantially fewer range queries compared to DBSCAN while still satisfying the cluster definition of DBSCAN. Moreover, by processing queries in block and merging the results into the current cluster structure, AnyDBC can be efficiently parallelized on shared memory architectures to further accelerate the performance, uniquely making it a parallel and anytime technique at the same time. Experiments show speedup factors of orders of magnitude compared to DBSCAN and its fastest variants as well as a high parallel scalability on multicore processors for very large real and synthetic complex datasets.
The Interaction of Architecture and Operating System Design
(1991)

Today’s high-performance RISC microprocessors have been highly tuned for integer and floating point application performance. These architectures have paid less attention to operating system requirements. At the same time, new operating system designs often have overlooked modern architectural trends which may unavoidably change the relative cost of certain primitive operations. The result is that operating system performance is well below application code performance on contemporary RISCS. This paper examines recent directions in computer architecture and operating systems, and the implications of changes in each domain for the other. The requirements of three components of operating system design are discussed in detail: interprocess communication, virt ual memory, and thread management. For each component, we relate operating system functional and performance needs to the mechanisms available on commercial RISC architectures such as the MIPS R2000 and R3000, Sun SPARC, IBM RS6000, Motorola 88000, and Intel i860. Our analysis reveals a number of specific reasons why the performance of operating system primitives on RISCS has not scaled with integer performance. In addition, we identify areas in which architectures could better (and cost-effectively) accommodate operating system needs, and areas in which operating system design could accommodate certain necessary characteristics of cost-effective highperformance microprocessors. This work was supported in part by the National Science Foundation under Grants No. CCR-8703049, CCR-8619663, and CCR-8907666, by the Washington Technology Center, by the Digital Equipment Corporation Systems Research Center and External Research Program, and by IBM and AT&T Fellowships. Bershad is now with the School of Computer Science, Carnegie Mellon University. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. 01991 ACM 0-89791 -380 -9/91 /0003 -0108 . ..$1 .50
Frappé: Functional Reactive Programming in Java
(2001)

Functional Reactive Programming (FRP) is a declarative programming model for constructing interactive applications based on a continuous model of time. FRP programs are described in terms of behaviors (continuous, timevarying, reactive values), and events (conditions that occur at discrete points in time). This paper presents Frappé, an implementation of FRP in the Java progamming language. The primary contribution of Frappé is its integration of the FRP event/behavior model with the Java Beans event/property model. At the interface level, any Java Beans component may be used as a source or sink for the FRP event and behavior combinators. This provides a mechanism for extending Frappé with new kinds of I/O connections and allows FRP to be used as a high-level declarative model for composing applications from Java Beans components. At the implementation level, the Java Beans event model is used internally by Frappé to propagate FRP events and changes to FRP behaviors. This allows Frappé applications to be packaged as Java Beans components for use in other applications, and yields an implementation of FRP well-suited to the requirements of event-driven applications (such as graphical user interfaces).
Colour spaces: perceptual, historical and applicational background
(2003)

Absnacr-In this paper we present an overview of colour spaces used In electrical engineering and image processing. We Streis the imparfance of the perceptual, historical and sppllcananal~hackgraund that led to a colour spare. The colour spaces presented are : RGB;opponentsolours rpsces, phenomenal colaur spscer, CMY, CMYK, TV colour s~aces (UW and YIQ), PhotoYCC, CIE XYZ, Lab and Luv colour spaces. Keywordr-colaur spaces, RGB, HSV, CIE